2D Geometry. Pierre Alliez Inria Sophia Antipolis
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1 2D Geometry Pierre Alliez Inria Sophia Antipolis
2 Outline Sample problems Polygons Graphs Convex hull Voronoi diagram Delaunay triangulation
3 Sample Problems
4 Line Segment Intersection Theorem: Segments (p 1,p 2 ) and (p 3,p 4 ) intersect in their interior iff p 1 and p 2 are on different sides of the line p 3 p 4 and p 3 and p 4 are on different sides of the line p p 1 p 2. 4 This can be checked by computing the orientations of four triangles. Which? Special cases: p 1 p 2 p 3
5 Computing the Intersection p( t) p ( p p ) t 0 t q( s) q ( q q ) s 0 s p 2 Question: What is the meaning of other values of s and t? q 1 q 2 Solve (2D) linear vector equation for t and s: p( t) q( s) check that t [0,1] and s [0,1] p 1
6 Nearest Neighbor Problem definition: Input: a set of points (sites) P in the plane and a query point q. Output: The point p P closest to q among all points in P. Rules of the game: One point set, multiple queries Applications: Store Locator Cellphones p q P
7 The Voronoi Diagram Problem definition: Input: a set of points (sites) P in the plane. S Output: A planar subdivision S into cells per site. The cell corresponding to p P contains all the points to which p is the closest. P
8 Point Location S Problem definition: Input: A partition S of the plane into cells and a query point p. p Output: The cell C S containing p. Rules of the game: One partition, multiple queries C Applications: Nearest neighbor State locator
9 Point in Polygon Problem definition: Input: a polygon P in the plane and a query point p. Output: true if p P, else false. P p Rules of the game: One polygon, multiple queries
10 Convex Hull Problem definition: Input: a set of points S in the plane. Output: Minimal convex polygon containing S. S CH(S)
11 Shortest Path Problem definition: Input: Obstacles locations and query endpoints s and t. t Output: the shortest path between s and t that avoids all obstacles. s Rules of the game: One obstacle set, multiple queries. Application: Robotics.
12 Range Searching and Counting Problem definition: Input: A set of points P in the plane and a query rectangle R P Output: (report) The subset Q P contained in R. (count) The size of Q. Rules of the game: One point set, multiple queries. Application: Urban planning. R Q
13 Visibility Problem definition: Input: a polygon P in the plane and a query point p. Output: Polygon Q P, visible to p. p P Q Rules of the game: One polygon, multiple queries Applications: Security
14 Ray Tracing
15 Polygons
16 Polygon Zoo convex convex with hole concave non-simple
17 Point in Polygon Given a polygon P with n sides, and a point q, decide whether q P. P q Solution A: Count how many times a ray r originating at q intersects P. Then q P iff this number is odd. Complexity: O(n) Question: Are there special cases? r
18 Art Gallery Problem Given a simple polygon P, say that two points p and q can see each other if the open segment pq lies entirely within P. A point guards a region R P if p sees all q R. p R p q Given a polygon P, what is the minimal number of guards required to guard P, and what are their locations?
19 Observations The entire interior of a convex polygon is visible from any interior point. A star-shaped polygon requires only one guard located in its kernel. convex non star-shaped star-shaped
20 Art Gallery Problem Easy Upper Bound n-2 guards suffice: Subdivide the polygon into n-2 triangles (triangulation) Place one guard in each triangle. Theorem: Any simple planar polygon with n vertices has a triangulation of size n-2.
21 Diagonals in Polygons A diagonal of a polygon P is a line segment connecting two vertices which lies entirely within P. Theorem: Any polygon with n>3 vertices has a diagonal, which may be found in O(n) time. Proof: Find the leftmost vertex v. Connect its two neighbors u and w. If this is not a diagonal there are other vertices inside the triangle uvw. Connect v with the vertex v furthest from the segment uw. v u v Question: Why not connect v with the second leftmost vertex? w
22 O(n 2 ) Polygon Triangulation Theorem: Every simple polygon with n vertices has a triangulation consisting of n-3 diagonals and n-2 triangles. Proof: By induction on n: Basis: A triangle (n=3) has a triangulation (itself) with no diagonals and one triangle. Induction: for a n+1 vertex polygon, construct a diagonal dividing the polygon into two polygons with n 1 and n 2 vertices such that n 1 +n 2-2=n. Triangulate the two parts of the polygon. There are now n 1-3+n 2-3+1=n- 3 diagonals and n 1-2+n 2-2=n-2 triangles.
23 Graphs
24 Graph Definitions A H J B L K C I D E G = <V,E> V = vertices = {A,B,C,D,E,F,G,H,I,J,K,L} E = edges = {(A,B),(B,C),(C,D),(D,E),(E,F),(F,G), (G,H),(H,A),(A,J),(A,G),(B,J),(K,F), (C,L),(C,I),(D,I),(D,F),(F,I),(G,K), (J,L),(J,K),(K,L),(L,I)} G F Vertex degree (valence) = number of edges incident on vertex. deg(j) = 4, deg(h) = 2 k-regular graph = graph whose vertices all have degree k A face of a graph is a cycle of vertices/edges which cannot be shortened. F = faces = {(A,H,G),(A,J,K,G),(B,A,J),(B,C,L,J),(C,I,J),(C,D,I), (D,E,F),(D,I,F),(L,I,F,K),(L,J,K),(K,F,G),(A,B,C,D,E,F,G,H)}
25 Connectivity A graph is connected if there is a path of edges connecting every two vertices. A graph is k-connected if between every two vertices there are k edge-disjoint paths. A graph G =<V,E > is a subgraph of a graph G=<V,E> if V is a subset of V and E is the subset of E incident on V. A connected component of a graph is a maximal connected subgraph. A subset V of V is an independent set in G if the subgraph it induces does not contain any edges of E.
26 Graph Embedding A graph is embedded in R d if each vertex is assigned a position in R d. Embedding in R 2 Embedding in R 3
27 Planar Graphs A planar graph is a graph whose vertices and edges can be embedded in R2 such that its edges do not intersect. Every planar graph can be drawn as a straight-line plane graph. Planar Graph Planar Graph Straight Line Plane Graph
28 Triangulation A triangulation is a straight line plane graph whose faces are all triangles. A Delaunay triangulation of a set of points is the unique set of triangles such that such that the circumcircle of any triangle does not contain any other point. The Delaunay triangulation avoids long and skinny triangles.
29 Topology v =12 f = 14 e = 25 c = 1 b = 1 Euler Formula For a planar graph: v+f-e = 2c-b v = # vertices f = # faces e = # edges c = # conn. comp. b = # boundaries
30 Convex Hull
31 Convexity and Convex Hull A set S is convex if any pair of points p,q S satisfy pq S. p p q q The convex hull of a set S is: The minimal convex set that contains S, i.e. any convex set C such that S C satisfies CH(S) C. The intersection of all convex sets that contain S. The set of all convex combinations of p i S, i.e. all points of the form: n p, 0, 1 i i i i i 1 i 1 n convex CH(S) S non-convex
32 Convex Hulls Some Facts The convex hull of a set is unique (up to colinearities). The boundary of the convex hull of a point set is a polygon on a subset of the points. S CH(S)
33 Convex Hull Naive Algorithm Description: For each pair of points construct its connecting segment and supporting line. Find all the segments whose supporting lines divide the plane into two halves, such that one half plane contains all the other points. Construct the convex hull out of these segments. Time complexity: All pairs: n nn ( 1) O O O n ( ) ( ) ( ) Check all points for each pair: O(n) Total: O(n 3 )
34 Possible Pitfalls Degenerate cases e.g. 3 collinear points. Might harm the correctness of the algorithm. Segments AB, BC and AC will all be included in the convex hull. A B C Numerical problems We might conclude that none of the three segments belongs to the convex hull.
35 Convex Hull Graham s Scan Algorithm: Sort the points according to their x coordinates. Construct the upper boundary by scanning the points in the sorted order and performing only right turns. Construct the lower boundary (with left turns ). Concatenate the two boundaries. Time Complexity: O(nlogn) May be implemented using a stack Question: How do we check for right turn?
36 The Algorithm Sort the points in increasing order of x-coord: p 1,.., p n. Push(S,p 1 ); Push(S,p 2 ); For i = 3 to n do While Size(S) 2 and Orient(p i,top(s),second(s)) 0 do Pop(S); Push(S,p i ); Print(S);
37 Graham s Scan Time Complexity Sorting O(n log n) If D i is number of points popped on processing p i, n n time ( D 1) n D i i 1 i 1 i Each point is pushed on the stack only once. Once a point is popped it cannot be popped again. Hence n i 1 D n i
38 Graham s Scan a Variant Algorithm: Find one point, p 0, which must be on the convex hull. Sort the other points by the angle of the rays to them from p 0. Question: Is it necessary to compute the actual angles? Construct the convex hull using one traversal of the points. Time Complexity: O(n log n) Question: What are the pros and cons of this algorithm relative to the previous?
39 Convex Hull - Divide and Conquer Algorithm: Find a point with a median x coordinate (time: O(n)) Compute the convex hull of each half (recursive execution) Combine the two convex hulls by finding common tangents. This can be done in O(n). Complexity: O(nlogn)
40 Finding Common Tangents
41 Finding Common Tangents
42 Finding Common Tangents
43 Finding Common Tangents
44 Finding Common Tangents
45 Finding Common Tangents
46 Finding Common Tangents
47 Finding Common Tangents
48 Finding Common Tangents
49 Finding Common Tangents
50 Finding Common Tangents
51 Finding Common Tangents
52 Finding Common Tangents
53 Finding Common Tangents
54 Finding Common Tangents To find lower tangent: Find a - the rightmost point of H A Find b the leftmost point of H B O(n) While ab is not a lower tangent for H A and H B, do: If ab is not a lower tangent to H A do a = a-1 If ab is not a lower tangent to H B do b = b-1
55 Output-Sensitive Convex Hull Gift Wrapping Algorithm: Find a point p 1 on the convex hull (e.g. the lowest point). Rotate counterclockwise a line through p 1 until it touches one of the other points (start from a horizontal orientation). Question: How is this done? Repeat the last step for the new point. Stop when p 1 is reached again. Time Complexity: O(nh), where n is the input size and h is the output (hull) size.
56 General Position When designing a geometric algorithm, we first make some simplifying assumptions, e.g. No 3 collinear points. No two points with the same x coordinate. etc. Later, we consider the general case: How should the algorithm react to degenerate cases? Will the correctness be preserved? Will the runtime remain the same?
57 Lower Bound for Convex Hull A reduction from sorting to convex hull is: Given n real values x i, generate n 2D points on the graph of a convex function, e.g. (x i,x i2 ). Compute the (ordered) convex hull of the points. The order of the convex hull points is the numerical order of the x i. So CH= (nlgn)
58 Voronoi Diagram and Delaunay triangulation
59 Voronoi Diagram
60 Voronoi Diagram The collection of the non-empty Voronoi regions and their faces, together with their incidence relations, constitute a cell complex called the Voronoi diagram of E. The locus of points which are equidistant to two sites pi and pj is called a bisector, all bisectors being affine subspaces of IR d (lines in 2D). demo
61 Voronoi Diagram A Voronoi cell of a site pi defined as the intersection of closed half-spaces bounded by bisectors. Implies: All Voronoi cells are convex. demo
62 Voronoi Diagram Voronoi cells may be unbounded with unbounded bisectors. Happens when a site pi is on the boundary of the convex hull of E. demo
63 Voronoi Diagram Voronoi cells have faces of different dimensions. In 2D, a face of dimension k is the intersection of 3 - k Voronoi cells. A Voronoi vertex is generically equidistant from three points, and a Voronoi edge is equidistant from two points.
64 Delaunay Triangulation Dual structure of the Voronoi diagram. The Delaunay triangulation of a set of sites E is a simplicial complex such that k+1 points in E form a Delaunay simplex if their Voronoi cells have nonempty intersection demo
65 Delaunay Triangulation The Delaunay triangulation of a point set E covers the convex hull of E.
66 Delaunay Triangulation canonical triangulation associated to any point set
67 Delaunay Triangulation: Local Property Empty circle: A triangulation T of a point set E such that any d- simplex of T has a circumsphere that does not enclose any point of E is a Delaunay triangulation of E. Conversely, any k-simplex with vertices in E that can be circumscribed by a hypersphere that does not enclose any point of E is a face of the Delaunay triangulation of E. demo
68 Delaunay Triangulation In 2D: «quality» triangulation Smallest triangle angle: The Delaunay triangulation of a point set E is the triangulation of E which maximizes the smallest angle. Even stronger: The triangulation of E whose angular vector is maximal for the lexicographic order is the Delaunay triangulation of E. good bad
69 Delaunay Triangulation Thales Theorem: Let C be a circle, and l a line intersecting C at points a and b. Let p, q, r and s be points lying on the same side of l, where p and q are on C, r inside C and s outside C. Then: q s arb 2 apb p aqb asb a l r b
70 Delaunay Triangulation Improving a triangulation: In any convex quadrangle, an edge flip is possible. If this flip improves the triangulation locally, it also improves the global triangulation. If an edge flip improves the triangulation, the first edge is called illegal.
71 Delaunay Triangulation Illegal edges: Lemma: An edge pq is illegal iff one of its opposite vertices is inside the circle defined by the other three vertices. Proof: By Thales theorem. p Theorem: A Delaunay triangulation does not contain illegal edges. Corollary: A triangle is Delaunay iff the circle through its vertices is empty of other sites (the empty-circle condition). Corollary: The Delaunay triangulation is not unique if more than three sites are co-circular. q
72 Delaunay Triangulation Duality on the paraboloid: Delaunay triangulation obtained by projecting the lower part of the convex hull.
73 Delaunay Triangulation z=x 2 +y 2 z=x 2 +y 2 z=x 2 +y 2 Project the 2D point set onto the 3D paraboloid Compute the 3D lower convex hull Project the 3D facets back to the plane.
74 Proof The intersection of a plane with the paraboloid is an ellipse whose projection to the plane is a circle. s lies within the circumcircle of p, q, r iff s lies on the lower side of the plane passing through p, q, r. r q s p p, q, r S form a Delaunay triangle iff p, q, r form a face of the convex hull of S. q s r p
75 Voronoi Diagram Given a set S of points in the plane, associate with each point p=(a,b) S the plane tangent to the paraboloid at p: z = 2ax+2by-(a2+b2). q p VD(S) is the projection to the (x,y) plane of the 1-skeleton of the convex polyhedron formed from the intersection of the halfspaces above these planes. q p
76 Delaunay Triangulation An naïve O(n 4 ) Construction Algorithm Repeat until impossible: Select a triple of sites. If the circle through them is empty of other sites, keep the triangle whose vertices are the triple.
77 The In-Circle Test Theorem: If a,b,c,d form a CCW convex polygon, then d lies in the circle determined by a, b and c iff: 2 2 ax ay ax a 1 y 2 2 x y x y 1 det b b b b cx cy cx cy dx d y dx d y 1 Proof: We prove that equality holds if the points are co-circular. There exists a center q and radius r such that: and similarly for b, c, d: ( a q ) ( a q ) r x x y y 2 2 ax a y ax ay b b x by b cx c y 2 2 d d x d y d x y 1 x y qx 2 qy ( qx qy r ) 0 c c x y 1 So these four vectors are linearly dependent, hence their det vanishes. Corollary: d circle(a,b,c) iff b circle(c,d,a) iff c circle(d,a,b) iff a circle(b,c,d)
78 Delaunay Triangulation Another naive construction: Start with an arbitrary triangulation. Flip any illegal edge until no more exist. Requires proof that there are no local minima. Could take a long time to terminate.
79 O(nlogn) Delaunay Triangulation Algorithm Incremental algorithm: Form bounding triangle which encloses all the sites. Add the sites one after another in random order and update triangulation. If the site is inside an existing triangle: Connect site to triangle vertices. Check if a 'flip' can be performed on one of the triangle edges. If so check recursively the neighboring edges. If the site is on an existing edge: Replace edge with four new edges. Check if a 'flip' can be performed on one of the opposite edges. If so check recursively the neighboring edges.
80 Flipping Edges A new vertex p r is added, causing the creation of edges. The legality of the edge p i p j (with opposite vertex) p k is checked. If p i p j is illegal, perform a flip, and recursively check edges p i p k and p j p k, the new edges opposite p r. p i p r p j Notice that the recursive call for p i p k cannot eliminate the edge p r p k. Note: All edge flips replace edges opposite the new vertex by edges incident to it! p k
81 Flipping Edges - Example p r p i p j p k
82 Number of Flips Theorem: The expected number of edges flips made in the course of the algorithm (some of which also disappear later) is at most 6n. Proof: During insertion of vertex p i, k i new edges are created: 3 new initial edges, and k i -3 due to flips. Backward analysis: E[k i ] = the expected degree of p i after the insertion is complete = 6 (Euler).
83 Algorithm Complexity Point location for every point: O(log n) time. Flips: (n) expected time in total (for all steps). Total expected time: O(n log n). Space: (n). demo
84 3D Delaunay Triangulation
85
86 Constrained Delaunay triangulation
87 2D Constrained Delaunay Triangulation Definition 1 : Let (P, S) be a PSLG. The constrained triangulation T(P, S) is constrained Delaunay iff the circumcircle of any triangle t of T encloses no vertex visible from a point in the relative interior of t.
88 2D Constrained Delaunay Triangulation Definition 2 : Let (P, S) be a PSLG. The constrained triangulation T(P, S) is constrained Delaunay iff any edge e of T is either a segment of S or is constrained Delaunay. Simplex e constrained Delaunay with respect to the PSLG (P, S) iff: int(e) S = 0 There exists a circumcircle of e that encloses no vertex visible from a point in the relative interior of e.
89 Pseudo-dual: Bounded Voronoi Diagram constrained Bounded Voronoi diagram blind triangles
90 2D Constrained Delaunay Triangulation Any PSLG (P, S) has a constrained Delaunay triangulation. If (P, S) has no degeneracy, this triangulation is unique.
91 Delaunay Filtering
92 Restricted Voronoi Diagram The Voronoi diagram restricted to a curve S, Vor S (E), is the set of edges of Vor(E) that intersect S. S
93 Restricted Delaunay Triangulation The restricted Delaunay triangulation restricted to a curve S is the set of edges of the Delaunay triangulation whose dual edges intersect S. (2D)
94 Restricted Delaunay Triangulation The restricted Delaunay triangulation restricted to a surface S is the set of triangles of the Delaunay triangulation whose dual edges intersect S.
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